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22nd of 8 in Learning and cookbooks

ai-engineering-from-scratch

Hands-on curriculum for building AI systems from math to agents

Website ↗ (opens in a new tab)Repository on GitHub ↗ (opens in a new tab)
Stars
68.4k
License
MIT
Last commit
Oct 2026
Last release
Oct 2026
Language
Python

Checked on GitHub.

Overview

A course of 528 lessons across 20 phases, covering math foundations, ML fundamentals, LLM engineering, agents, tools and protocols, and production infrastructure. Lessons are runnable code in Python, TypeScript, Rust and Julia, with three graded projects: retrieval evaluation, agent trace debugging and a tool-call firewall. It can be followed on the website, from a local clone, or through tutor skills installed into a coding agent.

Who it is for: Developers learning to build LLM, RAG and agent systems from first principles

Strengths

  • Lessons ship runnable code; the first ones need only python3 and no extra dependencies
  • Three staged projects with local graders (project_test.py --strict) and reference solutions
  • Tutor skills install via npx skills and save progress to LEARNING.md
  • Code in Python, TypeScript, Rust and Julia; README translated into 30+ languages

Weaknesses

  • 528 lessons is a large scope; total study time is not stated in the visible README
  • Project starters fail by design until you implement the stages
  • Tutor skills need Node.js, npx and a skill-capable coding agent host
  • GPU and RAM needs for later phases are not stated in the visible README

What it needs

  • Needs Python 3.10+, Node.js 22.18+ (TypeScript project), Rust (Tool Call Firewall project), npx (optional, tutor skills)

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